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008			220224s2022 sz | s |||| 0|eng d
020			_a9783030914790
024	7		_a10.1007/978-3-030-91479-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.8865 _b2022 EB
100	1		_aMittag, Gabriel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683723
245	1	0	_aDeep Learning Based Speech Quality Prediction _cby Gabriel Mittag
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 165 páginas) _b58 ilustraciones, 54 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aT-Labs Series in Telecommunication Services _x2192-2829
505	0		_a1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA - A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
520			_aThis book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.
988			_aSpringer_Engineering_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9155835 _aTelefonía por Internet
650		7	_2embne _9142152 _aVoz
776	0	8	_iPrinted edition: _z9783030914783
776	0	8	_iPrinted edition: _z9783030914806
776	0	8	_iPrinted edition: _z9783030914813
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-91479-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _esc _zSI